---
title: Welcome to LangCrew
description: Enterprise-grade multi-agent framework built on LangGraph - from prototyping to production
template: splash
hero:
  tagline: From CrewAI simplicity to LangGraph power - the only framework you need for production-ready AI agent systems
  actions:
    - text: Quick Start
      link: /guides/quickstart/
      icon: right-arrow
      variant: primary
    - text: GitHub
      link: https://github.com/01-ai/langcrew
      icon: github
---

import { Card, CardGrid, Badge } from '@astrojs/starlight/components';

<div class="hero-stats">
  <Badge text="Python 3.10+" variant="note" />
  <Badge text="Built on LangGraph" variant="success" />
  <Badge text="Production Ready" variant="caution" />
</div>

## Why Choose LangCrew?

**The best of both worlds:** CrewAI's simplicity meets LangGraph's enterprise capabilities

<CardGrid stagger>
	<Card title="Beyond Traditional Flexible Paradigms">
		**Revolutionary agent collaboration**: Provides a simple, highly configurable development experience with powerful built-in mechanisms like HITL, dynamic workflow orchestration, and event-driven processes that empower unprecedented agent coordination.
	</Card>
	<Card title="Full-Stack Support for Productization">
		**Accelerate market delivery**: Complete Agent-UI protocol and React component library enable frontend visualization of agent planning, scheduling, execution, and tool invocation—significantly reducing time from development to production deployment.
	</Card>
	<Card title="Application Templates for Fast Launch">
		**Industry-ready solutions**: Rich variety of production-tested templates enabling rapid prototyping and deployment of multi-agent solutions across diverse industries and scenarios—from concept to market in record time.
	</Card>
	<Card title="Integrated Development and Operations Support">
		**Seamless lifecycle management**: Integrated free SaaS services covering system construction, deep observability, sandbox environments, and deployment resources—simplifying the entire journey from development to operations.
	</Card>
</CardGrid>

## Framework Comparison

| Aspect            | LangGraph                | CrewAI              | LangCrew                                    |
| ----------------- | ------------------------ | ------------------- | ------------------------------------------- |
| **Abstraction**   | Low-level primitives     | High-level patterns | **High-level on LangGraph**                 |
| **Development**   | Build from scratch       | Simple but limited  | **Best practices pre-built**                |
| **HITL**          | Basic interrupt/resume   | Limited support     | **Advanced approval system + bilingual UI** |
| **Memory**        | Complete primitives/docs | Simple context      | **LangGraph native + langmem integration**  |
| **Tools**         | LangChain only           | Custom only         | **Unified registry + LangCrew-Tools**       |
| **UI**            | None                     | Basic examples      | **Full React components**                   |
| **Observability** | LangSmith integration    | Enterprise edition  | **LangSmith + LangTrace integration**                   |
| **Deployment**    | Platform available       | Enterprise edition  | **Platform (Coming Soon)**                  |


## Quick Start Example

**Build your first agent crew in under 5 minutes:**

```bash
# Install LangCrew
pip install langcrew

# Set up your LLM API key
export OPENAI_API_KEY=your_openai_key
```

```python
from langcrew import Agent, Crew, Task
from langcrew.project import CrewBase, agent, crew, task
from langchain_openai import ChatOpenAI

# Configure LLM
llm = ChatOpenAI(model="gpt-4o")

@CrewBase
class ResearchCrew:
    @agent
    def researcher(self) -> Agent:
        return Agent(
            role="Market Researcher",
            goal="Find latest trends in AI automation",
            tools=["web_search"],  # Built-in tools
            backstory="Expert at finding market insights",
            llm=llm
        )
    
    @task
    def research_task(self) -> Task:
        return Task(
            description="Research AI automation trends for {topic}",
            agent=self.researcher(),
            expected_output="Detailed market analysis report"
        )
    
    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            memory=True,  # Enable memory
            verbose=True  # See execution details
        )

# Run the crew
result = ResearchCrew().crew().kickoff(
    inputs={"topic": "enterprise AI adoption"}
)
```

## Getting Started Paths

<CardGrid>
	<Card title="Quick Start Guide">
		**New to LangCrew?** Begin with installation and build your first agent crew in minutes with our step-by-step guide.
		
		[Quick Start →](/guides/quickstart/)
	</Card>
	<Card title="Core Concepts">
		**Understand the framework** Learn about Agents, Tasks, Crews, and the foundational concepts that power multi-agent collaboration.
		
		[Agents →](/concepts/agents/) | [Tasks →](/concepts/tasks/) | [Crews →](/concepts/crews/)
	</Card>
	<Card title="Advanced Features">
		**Ready for production?** Master memory systems, human-in-the-loop workflows, tools integration, and web services.
		
		[Memory Guide →](/guides/memory/getting-started/) | [HITL Guide →](/guides/hitl/getting-started/)
	</Card>
	<Card title="Production Deployment (Coming Soon)">
		**Scale your applications** Advanced deployment features, enterprise observability, and production-grade configurations are being developed.
		
		[Web Services →](/guides/web/getting-started/) | [Tools →](/guides/tools/)
	</Card>
</CardGrid>

## Success Stories

> **"LangCrew reduced our development time from weeks to days. The production templates and UI components are game-changers."**  
> — *Sarah Chen, Lead AI Engineer at TechCorp*

> **"Finally, a framework that bridges the gap between prototyping and production. The HITL features are exactly what we needed."**  
> — *Alex Rodriguez, CTO at StartupAI*

---

<div style="text-align: center; margin: 2rem 0;">
  <strong>Ready to build production-ready AI agent systems?</strong><br/>
  <a href="/guides/quickstart/" style="margin: 0 1rem;">Get Started →</a>
  <a href="https://github.com/01-ai/langcrew" style="margin: 0 1rem;">Star on GitHub →</a>
</div>